{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Lifetime Value Analysis - Hopper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 100)\n",
    "sns.set_palette(\"husl\")\n",
    "sns.set(rc={'image.cmap': 'coolwarm'})\n",
    "%matplotlib inline\n",
    "\n",
    "from lifetimes.plotting import *\n",
    "from lifetimes.utils import *\n",
    "from lifetimes import BetaGeoFitter\n",
    "from lifetimes.plotting import plot_frequency_recency_matrix\n",
    "from lifetimes.plotting import plot_probability_alive_matrix\n",
    "from lifetimes.plotting import plot_period_transactions\n",
    "from lifetimes.utils import calibration_and_holdout_data\n",
    "from lifetimes.plotting import plot_calibration_purchases_vs_holdout_purchases\n",
    "from lifetimes.plotting import plot_history_alive\n",
    "from lifetimes import GammaGammaFitter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shopped     797239\n",
       "inactive    211847\n",
       "expired     148724\n",
       "booked        8764\n",
       "active        1571\n",
       "Name: status_latest, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "watch_df = pd.read_csv('search_sample_data/watch_sample.csv')\n",
    "watch_df['status_latest'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2018-01-01 00:00:00'), Timestamp('2019-09-17 00:00:00'))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booked_df = watch_df.loc[watch_df['status_latest'] == 'booked']\n",
    "cols_of_interest = ['user_id', 'latest_status_change_dt', 'lowest_price']\n",
    "booked_df = booked_df[cols_of_interest]\n",
    "booked_df = booked_df[pd.notnull(booked_df['lowest_price'])]\n",
    "\n",
    "booked_df['latest_status_change_dt'] = pd.to_datetime(booked_df['latest_status_change_dt'])\n",
    "booked_df['latest_status_change_dt'] = booked_df['latest_status_change_dt'].dt.date\n",
    "booked_df['latest_status_change_dt'] = pd.to_datetime(booked_df['latest_status_change_dt'])\n",
    "booked_df['latest_status_change_dt'].min(), booked_df['latest_status_change_dt'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4596"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booked_df['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>latest_status_change_dt</th>\n",
       "      <th>lowest_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>4a7ab9b3a1c10f8b7036e55605a758a5c7654e1682b057...</td>\n",
       "      <td>2018-05-02</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>338</td>\n",
       "      <td>21557c4bf83241450f6f23baf1c949fd8d5b13abc401cd...</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>640</td>\n",
       "      <td>fe895b28195094b405b5d887c894e1b3cdde9945047baa...</td>\n",
       "      <td>2018-02-24</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>678</td>\n",
       "      <td>2929c98ea8d72bd251025ef1ff232ca9a3a38483c18fd4...</td>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>725</td>\n",
       "      <td>c6a8984d9c1f975afe69bd162b242830bbb7310f4c9204...</td>\n",
       "      <td>2018-06-21</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1361</td>\n",
       "      <td>83e2bb025505e996626e3fac10da0d16785c3e1bae1f1d...</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1364</td>\n",
       "      <td>92e9c28aeeaa9c19618d9c8ef77a48ff6079dd0ed0c36d...</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1512</td>\n",
       "      <td>9c5be29b757ce0e34685f630e3f2c9323a0ba3b9b96505...</td>\n",
       "      <td>2018-01-16</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1619</td>\n",
       "      <td>eaf5f71b19d612441bb959593778fafe9b4ac1ad0fc486...</td>\n",
       "      <td>2018-07-19</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1636</td>\n",
       "      <td>59b034a8d3b262d35e89b096c2cdac072e59a451bd022a...</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                user_id  \\\n",
       "114   4a7ab9b3a1c10f8b7036e55605a758a5c7654e1682b057...   \n",
       "338   21557c4bf83241450f6f23baf1c949fd8d5b13abc401cd...   \n",
       "640   fe895b28195094b405b5d887c894e1b3cdde9945047baa...   \n",
       "678   2929c98ea8d72bd251025ef1ff232ca9a3a38483c18fd4...   \n",
       "725   c6a8984d9c1f975afe69bd162b242830bbb7310f4c9204...   \n",
       "1361  83e2bb025505e996626e3fac10da0d16785c3e1bae1f1d...   \n",
       "1364  92e9c28aeeaa9c19618d9c8ef77a48ff6079dd0ed0c36d...   \n",
       "1512  9c5be29b757ce0e34685f630e3f2c9323a0ba3b9b96505...   \n",
       "1619  eaf5f71b19d612441bb959593778fafe9b4ac1ad0fc486...   \n",
       "1636  59b034a8d3b262d35e89b096c2cdac072e59a451bd022a...   \n",
       "\n",
       "     latest_status_change_dt  lowest_price  \n",
       "114               2018-05-02         391.0  \n",
       "338               2018-02-09         359.0  \n",
       "640               2018-02-24         627.0  \n",
       "678               2018-05-06         267.0  \n",
       "725               2018-06-21         192.0  \n",
       "1361              2018-08-31         137.0  \n",
       "1364              2018-04-17         113.0  \n",
       "1512              2018-01-16          86.0  \n",
       "1619              2018-07-19         194.0  \n",
       "1636              2018-12-18         172.0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booked_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>001a25f124e240f5372e8c73117c54bd1b1ae6339bf864100a9abd1ddafa49ff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0067a6541cc78bd52ba0b9a4216e7f0e27e6cf7c6847a6dbbaf8b53194c01943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>459.0</td>\n",
       "    </tr>\n",
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       "      <td>00770c43298f4cd9ee3dfe37882825be327db3dca9affd9074639c9b1c9d7907</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>007de8bbd4f4dea2de20d00f3b904e08210d94a6215e0f57b1817711838a9b05</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>00874310e86ae74c1b470ec0f766493ed2f8949bbc004715d8e72bd70d97d1c7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>412.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>575.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>00c7f503021d4abdde4878c2b0caa9cfdcdc0c2cccd154ca174afedb66a152ad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>00c95bd6cd9a5f430df8a6d3494c8479145294c1ebed078c26ccd19731b39ef2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>401.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    frequency  recency      T  \\\n",
       "user_id                                                                         \n",
       "001a25f124e240f5372e8c73117c54bd1b1ae6339bf8641...        1.0     39.0  275.0   \n",
       "0067a6541cc78bd52ba0b9a4216e7f0e27e6cf7c6847a6d...        0.0      0.0  556.0   \n",
       "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...        3.0    268.0  455.0   \n",
       "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...        2.0    174.0  490.0   \n",
       "007de8bbd4f4dea2de20d00f3b904e08210d94a6215e0f5...        0.0      0.0  488.0   \n",
       "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...        1.0      5.0  602.0   \n",
       "00a143d91b4c16f3962dbbb4cbe76814c5fdd3104feecfb...        0.0      0.0  575.0   \n",
       "00ad4737c47b868cb87f461d899c7a5720f8dd64aa8e1d3...        0.0      0.0  224.0   \n",
       "00c7f503021d4abdde4878c2b0caa9cfdcdc0c2cccd154c...        0.0      0.0  578.0   \n",
       "00c95bd6cd9a5f430df8a6d3494c8479145294c1ebed078...        0.0      0.0  401.0   \n",
       "\n",
       "                                                    monetary_value  \n",
       "user_id                                                             \n",
       "001a25f124e240f5372e8c73117c54bd1b1ae6339bf8641...           602.0  \n",
       "0067a6541cc78bd52ba0b9a4216e7f0e27e6cf7c6847a6d...             0.0  \n",
       "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...           459.0  \n",
       "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...           152.0  \n",
       "007de8bbd4f4dea2de20d00f3b904e08210d94a6215e0f5...             0.0  \n",
       "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...           412.0  \n",
       "00a143d91b4c16f3962dbbb4cbe76814c5fdd3104feecfb...             0.0  \n",
       "00ad4737c47b868cb87f461d899c7a5720f8dd64aa8e1d3...             0.0  \n",
       "00c7f503021d4abdde4878c2b0caa9cfdcdc0c2cccd154c...             0.0  \n",
       "00c95bd6cd9a5f430df8a6d3494c8479145294c1ebed078...             0.0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = summary_data_from_transaction_data(booked_df, 'user_id', 'latest_status_change_dt', monetary_value_col='lowest_price', observation_period_end='2019-09-17')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>latest_status_change_dt</th>\n",
       "      <th>lowest_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>610019</td>\n",
       "      <td>00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...</td>\n",
       "      <td>2019-03-14</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>692114</td>\n",
       "      <td>00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...</td>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>749249</td>\n",
       "      <td>00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...</td>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886100</td>\n",
       "      <td>00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...</td>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1033859</td>\n",
       "      <td>00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...</td>\n",
       "      <td>2018-06-21</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   user_id  \\\n",
       "610019   00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...   \n",
       "692114   00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...   \n",
       "749249   00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...   \n",
       "886100   00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...   \n",
       "1033859  00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32b...   \n",
       "\n",
       "        latest_status_change_dt  lowest_price  \n",
       "610019               2019-03-14         628.0  \n",
       "692114               2019-02-22          79.0  \n",
       "749249               2018-06-19         707.0  \n",
       "886100               2019-02-22         151.0  \n",
       "1033859              2018-06-21         519.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booked_df.loc[booked_df['user_id'] == '00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc972b09da4c786dd69']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifetimes.BetaGeoFitter: fitted with 4596 subjects, a: 5.72, alpha: 40.51, b: 8.89, r: 0.19>\n"
     ]
    }
   ],
   "source": [
    "bgf = BetaGeoFitter(penalizer_coef=0.0)\n",
    "bgf.fit(data['frequency'], data['recency'], data['T'])\n",
    "print(bgf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_frequency_recency_matrix(bgf);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_probability_alive_matrix(bgf);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_period_transactions(bgf);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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       "                                                    frequency  recency      T  \\\n",
       "user_id                                                                         \n",
       "3b8991978a75d389403af0c972d743f7d1cbdae04b26e49...        4.0    444.0  548.0   \n",
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       "\n",
       "                                                    monetary_value  \\\n",
       "user_id                                                              \n",
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       "d4b3f8ddf91c4fbeb9b709eec30710c31c7643e5b5f647a...      363.800000   \n",
       "\n",
       "                                                    predicted_bookings  \n",
       "user_id                                                                 \n",
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      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 180\n",
    "data['predicted_bookings'] = bgf.conditional_expected_number_of_purchases_up_to_time(t, data['frequency'], data['recency'], data['T'])\n",
    "data.sort_values(by='predicted_bookings').tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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       "                                                   user_id  \\\n",
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       "\n",
       "        latest_status_change_dt  lowest_price  \n",
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       "515474               2018-01-22         237.0  \n",
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       "428732               2019-06-21         614.0  \n",
       "937250               2019-07-31          50.0  "
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     "execution_count": 98,
     "metadata": {},
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   "source": [
    "booked_df.loc[booked_df['user_id'] == 'd4b3f8ddf91c4fbeb9b709eec30710c31c7643e5b5f647a87b66b262972bb09c'].sort_values(by='latest_status_change_dt')"
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  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
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       "      <td>9.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>219.777778</td>\n",
       "      <td>0.001178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    frequency  recency      T  \\\n",
       "user_id                                                                         \n",
       "22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a7...        5.0     38.0  619.0   \n",
       "04512833af6252e50c905b48b71a6200f046979a847014e...       13.0    224.0  608.0   \n",
       "c3c3abc601cb2ded507fa1947eff4573b696d31d594ccf9...        4.0     21.0  597.0   \n",
       "ce6df06d073b99bb58fc9cc2609cba0a311da1182859859...        6.0     65.0  515.0   \n",
       "3d15e2b242b6aea4eba2e0c60625ec19872e2ad3f320790...        4.0     37.0  616.0   \n",
       "0c157eee84a21ea2fae4fc9ce2f68bdc0731b22551a0649...        7.0    119.0  565.0   \n",
       "8142f03b33c2cc16937ab978105ebdf2886f88ee9728bf0...        4.0     41.0  587.0   \n",
       "c656a4defb079d1622dd7a7a849bbddc98612ff34dc29dd...        4.0     50.0  606.0   \n",
       "690cb0c5d547c00ddb1abc2b7ab1eead794f267f9ccec22...        7.0    110.0  477.0   \n",
       "8be422972096454b2ec769d382920d0cbc772147cc8c441...        9.0    215.0  601.0   \n",
       "\n",
       "                                                    monetary_value  \\\n",
       "user_id                                                              \n",
       "22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a7...      309.000000   \n",
       "04512833af6252e50c905b48b71a6200f046979a847014e...      304.230769   \n",
       "c3c3abc601cb2ded507fa1947eff4573b696d31d594ccf9...      138.250000   \n",
       "ce6df06d073b99bb58fc9cc2609cba0a311da1182859859...      161.000000   \n",
       "3d15e2b242b6aea4eba2e0c60625ec19872e2ad3f320790...      224.000000   \n",
       "0c157eee84a21ea2fae4fc9ce2f68bdc0731b22551a0649...      334.285714   \n",
       "8142f03b33c2cc16937ab978105ebdf2886f88ee9728bf0...      236.500000   \n",
       "c656a4defb079d1622dd7a7a849bbddc98612ff34dc29dd...      277.000000   \n",
       "690cb0c5d547c00ddb1abc2b7ab1eead794f267f9ccec22...      173.857143   \n",
       "8be422972096454b2ec769d382920d0cbc772147cc8c441...      219.777778   \n",
       "\n",
       "                                                    predicted_bookings  \n",
       "user_id                                                                 \n",
       "22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a7...            0.000040  \n",
       "04512833af6252e50c905b48b71a6200f046979a847014e...            0.000068  \n",
       "c3c3abc601cb2ded507fa1947eff4573b696d31d594ccf9...            0.000111  \n",
       "ce6df06d073b99bb58fc9cc2609cba0a311da1182859859...            0.000124  \n",
       "3d15e2b242b6aea4eba2e0c60625ec19872e2ad3f320790...            0.000252  \n",
       "0c157eee84a21ea2fae4fc9ce2f68bdc0731b22551a0649...            0.000284  \n",
       "8142f03b33c2cc16937ab978105ebdf2886f88ee9728bf0...            0.000390  \n",
       "c656a4defb079d1622dd7a7a849bbddc98612ff34dc29dd...            0.000521  \n",
       "690cb0c5d547c00ddb1abc2b7ab1eead794f267f9ccec22...            0.000647  \n",
       "8be422972096454b2ec769d382920d0cbc772147cc8c441...            0.001178  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='predicted_bookings').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>latest_status_change_dt</th>\n",
       "      <th>lowest_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>583430</td>\n",
       "      <td>22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...</td>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>430237</td>\n",
       "      <td>22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>420343</td>\n",
       "      <td>22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>562055</td>\n",
       "      <td>22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>556.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>228909</td>\n",
       "      <td>22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124851</td>\n",
       "      <td>22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...</td>\n",
       "      <td>2018-02-13</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  user_id  \\\n",
       "583430  22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...   \n",
       "430237  22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...   \n",
       "420343  22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...   \n",
       "562055  22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...   \n",
       "228909  22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...   \n",
       "124851  22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a...   \n",
       "\n",
       "       latest_status_change_dt  lowest_price  \n",
       "583430              2018-01-06         749.0  \n",
       "430237              2018-01-08          82.0  \n",
       "420343              2018-01-10         125.0  \n",
       "562055              2018-01-22         556.0  \n",
       "228909              2018-02-05         688.0  \n",
       "124851              2018-02-13          94.0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booked_df.loc[booked_df['user_id'] == '22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a70f128fc062c0d3305'].sort_values(by='latest_status_change_dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    frequency_cal  \\\n",
      "user_id                                                             \n",
      "0067a6541cc78bd52ba0b9a4216e7f0e27e6cf7c6847a6d...            0.0   \n",
      "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...            1.0   \n",
      "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...            2.0   \n",
      "007de8bbd4f4dea2de20d00f3b904e08210d94a6215e0f5...            0.0   \n",
      "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...            1.0   \n",
      "\n",
      "                                                    recency_cal  T_cal  \\\n",
      "user_id                                                                  \n",
      "0067a6541cc78bd52ba0b9a4216e7f0e27e6cf7c6847a6d...          0.0  255.0   \n",
      "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...          2.0  154.0   \n",
      "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...        174.0  189.0   \n",
      "007de8bbd4f4dea2de20d00f3b904e08210d94a6215e0f5...          0.0  187.0   \n",
      "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...          5.0  301.0   \n",
      "\n",
      "                                                    frequency_holdout  \\\n",
      "user_id                                                                 \n",
      "0067a6541cc78bd52ba0b9a4216e7f0e27e6cf7c6847a6d...                0.0   \n",
      "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...                2.0   \n",
      "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...                0.0   \n",
      "007de8bbd4f4dea2de20d00f3b904e08210d94a6215e0f5...                0.0   \n",
      "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...                0.0   \n",
      "\n",
      "                                                    duration_holdout  \n",
      "user_id                                                               \n",
      "0067a6541cc78bd52ba0b9a4216e7f0e27e6cf7c6847a6d...               301  \n",
      "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...               301  \n",
      "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...               301  \n",
      "007de8bbd4f4dea2de20d00f3b904e08210d94a6215e0f5...               301  \n",
      "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...               301  \n"
     ]
    }
   ],
   "source": [
    "summary_cal_holdout = calibration_and_holdout_data(booked_df, 'user_id', 'latest_status_change_dt',\n",
    "                                        calibration_period_end='2018-11-20',\n",
    "                                        observation_period_end='2019-09-17' )   \n",
    "print(summary_cal_holdout.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f288116d2b0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgf.fit(summary_cal_holdout['frequency_cal'], summary_cal_holdout['recency_cal'], summary_cal_holdout['T_cal'])\n",
    "plot_calibration_purchases_vs_holdout_purchases(bgf, summary_cal_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "user = 'd4b3f8ddf91c4fbeb9b709eec30710c31c7643e5b5f647a87b66b262972bb09c' \n",
    "days_since_birth = 365\n",
    "sp_trans = booked_df.loc[booked_df['user_id'] == user]\n",
    "plot_history_alive(bgf, days_since_birth, sp_trans, 'latest_status_change_dt');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f282fae42b0>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user = '22aec3ce1c62b59aa956d4ede63f5e660c2d457eabab8a70f128fc062c0d3305' \n",
    "days_since_birth = 365\n",
    "sp_trans = booked_df.loc[booked_df['user_id'] == user]\n",
    "plot_history_alive(bgf, days_since_birth, sp_trans, 'latest_status_change_dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    frequency  recency      T  \\\n",
      "user_id                                                                         \n",
      "001a25f124e240f5372e8c73117c54bd1b1ae6339bf8641...        1.0     39.0  275.0   \n",
      "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...        3.0    268.0  455.0   \n",
      "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...        2.0    174.0  490.0   \n",
      "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...        1.0      5.0  602.0   \n",
      "00eaf2f44b992ddf993559d9f1cf2f22c56e4ba4e0c81a8...        1.0    210.0  432.0   \n",
      "\n",
      "                                                    monetary_value  \\\n",
      "user_id                                                              \n",
      "001a25f124e240f5372e8c73117c54bd1b1ae6339bf8641...           602.0   \n",
      "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...           459.0   \n",
      "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...           152.0   \n",
      "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...           412.0   \n",
      "00eaf2f44b992ddf993559d9f1cf2f22c56e4ba4e0c81a8...           310.0   \n",
      "\n",
      "                                                    predicted_bookings  \n",
      "user_id                                                                 \n",
      "001a25f124e240f5372e8c73117c54bd1b1ae6339bf8641...            0.128205  \n",
      "00702b0d3e9ad967e0df6b9f062a5cf5b83524d10ae32bc...            0.273681  \n",
      "00770c43298f4cd9ee3dfe37882825be327db3dca9affd9...            0.120243  \n",
      "00874310e86ae74c1b470ec0f766493ed2f8949bbc00471...            0.018677  \n",
      "00eaf2f44b992ddf993559d9f1cf2f22c56e4ba4e0c81a8...            0.166260  \n"
     ]
    }
   ],
   "source": [
    "returning_customers_summary = data[data['frequency']>0]\n",
    "\n",
    "print(returning_customers_summary.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>monetary_value</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.00446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>frequency</td>\n",
       "      <td>-0.00446</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                monetary_value  frequency\n",
       "monetary_value         1.00000   -0.00446\n",
       "frequency             -0.00446    1.00000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returning_customers_summary[['monetary_value', 'frequency']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    4596.000000\n",
      "mean        0.778503\n",
      "std         1.609312\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         1.000000\n",
      "max        26.000000\n",
      "Name: frequency, dtype: float64\n",
      "0.6303307223672759\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['frequency'].plot(kind='hist', bins=50)\n",
    "print(data['frequency'].describe())\n",
    "print(sum(data['frequency'] == 0)/float(len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
